Rich Prosody Diversity Modelling with Phone-level Mixture Density Network
This work addresses the problem of limited prosody diversity in text-to-speech synthesis for applications requiring natural and varied speech output, representing an incremental improvement over prior methods.
The paper tackled the challenge of generating natural speech with diverse prosody patterns by proposing a phone-level mixture density network using GMM, which improved naturalness and significantly enhanced prosody diversity in synthetic speech on the LJSpeech dataset.
Generating natural speech with diverse and smooth prosody pattern is a challenging task. Although random sampling with phone-level prosody distribution has been investigated to generate different prosody patterns, the diversity of the generated speech is still very limited and far from what can be achieved by human. This is largely due to the use of uni-modal distribution, such as single Gaussian, in the prior works of phone-level prosody modelling. In this work, we propose a novel approach that models phone-level prosodies with GMM based mixture density network (GMM-MDN). Experiments on the LJSpeech dataset demonstrate that phone-level prosodies can precisely control the synthetic speech and GMM-MDN can generate more natural and smooth prosody pattern than a single Gaussian. Subjective evaluations further show that the proposed approach not only achieves better naturalness, but also significantly improves the prosody diversity in synthetic speech without the need of manual control.